def testPeriodicResampleShapeInference(self):
with self.cached_session() as sess:
# Case 1: output shape can be fully inferreed.
x = array_ops.placeholder(dtypes.float32, shape=(2, 2, 4))
output = periodic_resample(x, [4, 4, None])
self.assertEqual(output.shape, [4, 4, 1])
# Case 2: output shape can not be inferred - report desired shape.
x = array_ops.placeholder(dtypes.float32, shape=(2, 2, None))
output = periodic_resample(x, [4, 4, None])
self.assertTrue(output.shape.is_compatible_with([4, 4, None]))
self.assertEqual(output.shape[2].value, None)
def testPeriodicResampleErrors(self):
input_tensor = numpy.zeros(shape=[1, 2, 2, 4])
with self.cached_session():
with self.assertRaisesWithPredicateMatch(
errors_impl.InvalidArgumentError,
'Dimension 3 input tensor has size 4, desired shape has size 1'
):
periodic_resample(input_tensor, [None, 4, 4, 1]).eval()
with self.assertRaisesWithPredicateMatch(
errors_impl.InvalidArgumentError,
'4, to be the same as the length of the desired shape, 3'):
periodic_resample(input_tensor, [None, 4, 4]).eval()
def testPeriodicResampleBasic4D(self):
input_tensor = numpy.arange(2 * 2 * 2 * 8).reshape((2, 2, 2, 8))
desired_shape = numpy.array([4, 4, 4, None])
output_tensor = numpy.array([[[[0], [4], [8], [12]],
[[2], [6], [10], [14]],
[[16], [20], [24], [28]],
[[18], [22], [26], [30]]],
[[[1], [5], [9], [13]],
[[3], [7], [11], [15]],
[[17], [21], [25], [29]],
[[19], [23], [27], [31]]],
[[[32], [36], [40], [44]],
[[34], [38], [42], [46]],
[[48], [52], [56], [60]],
[[50], [54], [58], [62]]],
[[[33], [37], [41], [45]],
[[35], [39], [43], [47]],
[[49], [53], [57], [61]],
[[51], [55], [59], [63]]]])
# NOTE: output_tensor != input_tensor.reshape((4, 4, 4, -1))
with self.cached_session():
variables.global_variables_initializer().run()
result = periodic_resample(input_tensor, desired_shape).eval()
self.assertAllEqual(result, output_tensor)
def testPeriodicResampleTruncatedBasic2D(self):
input_tensor = numpy.arange(12).reshape((3, 4))
desired_shape = numpy.array([5, None])
output_tensor = input_tensor.reshape((6, 2))[:-1]
with self.cached_session():
variables.global_variables_initializer().run()
result = periodic_resample(input_tensor, desired_shape).eval()
self.assertAllEqual(result, output_tensor)
def testPeriodicResampleGradient(self):
desired_shape = numpy.array([4, 4, None])
result_shape = (4, 4, 1)
input_shape = (2, 2, 4)
with self.cached_session() as sess:
x = array_ops.placeholder(dtypes.float32, shape=input_shape)
output = periodic_resample(x, desired_shape)
error = gradient_checker.compute_gradient_error(
x, input_shape, output, result_shape)
self.assertLess(error, 1e-4)
def testPeriodicResampleBasic3D(self):
input_tensor = numpy.arange(2 * 2 * 4).reshape((2, 2, 4))
desired_shape = numpy.array([4, 4, None])
output_tensor = numpy.array([[[0], [2], [4], [6]], [[1], [3], [5],
[7]],
[[8], [10], [12], [14]],
[[9], [11], [13], [15]]])
# NOTE: output_tensor != input_tensor.reshape((4, 4, -1))
with self.cached_session():
variables.global_variables_initializer().run()
result = periodic_resample(input_tensor, desired_shape).eval()
# input_tensor[0, 0, 0] == result[0, 0, 0]
# input_tensor[0, 0, 1] == result[1, 0, 0]
# input_tensor[0, 0, 2] == result[0, 1, 0]
# input_tensor[0, 0, 3] == result[1, 1, 0]
self.assertAllEqual(result, output_tensor)